The present invention generally relates to a method of detecting critical objects out of video produced in a plurality of CCTV cameras.
More specifically, the present invention relates to a method of detecting critical objects out of CCTV video, in which lightweight algorithms of foreground segmentation and blob analysis are applied to the CCTV video in order to detect a plurality of moving objects and then object filtering is performed based on distribution characteristics of metadata in order to propose critical objects contained in the CCTV video, whereby a few numbers of staff members in the integrated control center may efficiently monitor display screen of a large number of CCTV cameras.
Recently, pluralities of CCTV cameras are installed for the purpose of crime prevention as well as proof of criminal evidence. The videos taken in these CCTV cameras are delivered to police stations or integrated control center, in which staff members monitor the videos for detecting any possible abnormal behavior or criminal evidence.
As the number of CCTV cameras has rapidly increased, the number of staff members becomes not enough to appropriately handle the cameras. According to the National Police Agency data of 2011, the staff members of Seoul are monitoring 45 CCTV cameras per people in average, which renders the crime prevention uneffective.
In the course of criminal investigation, criminal tracing or lost-child search, it may be necessary to check CCTV storage video. The CCTV storage video has been provided from a plurality of CCTV cameras and stored in a storage device for a period of time (e.g., 7 days). In this case, a small number of staff members are conventionally looking over the massive videos. This type of work is far from efficient.
However, it is impractical to expand the staff members for solving the above-mentioned problem. Alternatively, the intelligent control system has been proposed in which the CCTV videos are analyzed by utilizing image processing technology so as to detect and present objects on which staff members may have attention (i.e., critical objects).
In conventional intelligent control systems, it has been researched on optimal arrangement of cameras, data acquision and storage, feature extraction, object detection, and video data interpretation and visualization, and video analysis. In specific, for commercial usage, automatic identification feature of the intelligent control system shall be good enough to replace operations of staff members. For that purpose, it has been researched on requirements of road traffic situation control center or CCTV integrated control center as well as on human behavior recognition.
As such, the conventional intelligent control systems are generally designed to detect specific behaviors out of CCTV video, e.g., fighting or collapsing. In order to identify behaviors of objects exquisitely enough to replace staff members, the conventional intelligent control systems have adopted heavy algorithms in processing the CCTV video. It means very high-performance image analysis server is required.
The above-described conventional art is unappropriate for the integrated control center in which videos from thousands of CCTV cameras are collected and analyzed. In order to appropriately perform the heavy algorithms on massive amount of high-resolution CCTV videos, the intelligent control system shall be equipped with a very high-performance image analysis server or alternatively equipped with a many numbers of image analysis servers. That is why the intelligent control system is not widely adopted in the integrated control center.